Parallel-Based Techniques for Managing and Analyzing the Performance on Semantic Graph

In recent years, data are generated rapidly that advanced the evolving of the linked data. Modern data are globally distributed over the semantically linked graphs. The nature of the distributed data over the semantic graph raised new demands on further investigation on improving performance on the semantic graphs. In this work, we analyzed the time latency as an important factor to be further investigated and improved. We evaluated the parallel computing on these distributed data in order to better utilize the parallelism approaches. A federation framework based on a multi-threaded environment supporting federated SPARQL query was introduced. In our experiments, we show the achievability and effectiveness of our model on a set of real-world quires through real-world Linked Open Data cloud. Significant performance improvement has noticed. Further, we highlight short-comings that could open an avenue in the research of federated queries. Keywords: Semantic web; distributed query processing; query federation; linked data; join methods.